Ali Najmi

A conceptual model for measuring supply chain's performance

In this article, a conceptual model for measuring supply chain (SC) performance is proposed which can be used for most organisations with the same class at various industries. The model has been developed according to performance metrics interdependencies and some existing shortcomings in the available literature of performance models. Furthermore, it has tried to see the key features of a performance evaluation model. The methodology which is used for solving and integrating the model is a combination of the analytical hierarchy process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) methods. The DEMATEL and AHP are used for understanding the relationship between comparison metrics and integration to provide a value for performance, respectively.

Reviewing the transport domain: an evolutionary bibliometrics and network analysis

Tracing the trajectory of scientific fields has been recognized by informaticians, nonetheless, little effort has been dedicated to understanding the evolution of the fast-moving research field of transport, quantitatively and qualitatively. This paper identifies intellectual turning points and emerging trends in the area of transport. Using bibliometric methods, co-keyword networks, journal co-citation networks, highly cited categories, and country and institute networks are detected, visualized and discussed. To conduct this analysis, all publications (35,712) in 23 top journals in the field of transport are extracted from the Institute for Scientific Information (Web of Science). The output of this article could be a valuable source for academics and practitioners working in the field of transport planning and those who work in the areas having a strong relationship with transport issues including mathematicians, economics, operation research, management and geography.

How Should Travel Demand and Supply Models Be Jointly Calibrated

Calibration is a critical aspect of model development that has long been recognized by researchers as a challenging issue. In particular, difficulties arise when the observed data used for calibration do not match the model output, which is the case in the majority of transport planning models. In the traditional calibration process, the origin–destination (OD) matrices are the key interface between demand and supply models, which could lead to issues when observed traffic link counts are used to update the OD matrix, causing a loss of key demand characteristics in the process. Developing a unified structure for modeling both demand and supply requires a calibration process that meets the requirements of both types of models, a serious issue which has received less attention in the literature. In this paper, the existing processes of developing and integrating demand and supply models are discussed and then examined using a case study in the Melbourne area. The numerical results show that the standard OD calibration procedure causes unrealistic changes in the OD matrix. Finally, some possible solutions to address the current limitations in development of a unified structure are discussed.

Energy consumption in the residential sector: a study on critical factors

In recent years, lots of efforts have been devoted to the identification of the factors influencing residential energy consumption. Many factors affect energy consumption at the same time, leading to the lack of precision when identifying which factors are significant. This paper reports the results of performing factor analysis for examining the factors affecting residential energy consumption. Data gathered through interviews and surveys with the residents and of housing units in Tehran (capital of Iran) are used for this purpose. The database applied comprises 56 predictors, for 2087 observations. Thirteen latent factors related to households’ energy consumption were shown by the data. Finally, a regression model was employed in order to recognise the most important factors. The amount of electricity and natural gas consumption was used as the dependent variable in the regression model. The results obtained can help prioritise efforts for modifying parameters in order to reduce the energy consumption in the residential sector.